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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
# Streamlit Page Config
st.set_page_config(page_title="SVM Classifier", layout="wide")
st.title("🔬 SVM Classifier")
# Intro Section
st.markdown("""
## 🤖 What is a Support Vector Machine (SVM)?
Support Vector Machine is a powerful classification algorithm that works by finding the optimal decision boundary (hyperplane) that best separates different classes.
### Key Features:
- Maximizes the margin between classes
- Uses support vectors — data points closest to the margin
- Can handle linear and non-linear data using **kernels**
---
## 📊 Dataset: Breast Cancer Diagnosis
We’ll classify tumors as **Malignant (1)** or **Benign (0)** based on features from cell nuclei in digitized images.
""")
# Load Dataset
@st.cache_data
def load_data():
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df["target"] = data.target
return df, data
df, data_info = load_data()
# Show Data
st.subheader("🔍 Data Preview")
st.dataframe(df.head(), use_container_width=True)
# Sidebar Settings
st.sidebar.header("⚙️ SVM Settings")
kernel = st.sidebar.selectbox("Kernel Type", ["linear", "rbf", "poly"])
C = st.sidebar.slider("Regularization (C)", min_value=0.01, max_value=10.0, value=1.0)
# Preprocess
X = df.drop("target", axis=1)
y = df["target"]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
# Model Training
model = SVC(kernel=kernel, C=C, probability=True, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# Results
acc = accuracy_score(y_test, y_pred)
st.success(f"✅ Accuracy: {acc * 100:.2f}%")
st.markdown("### 📋 Classification Report")
st.text(classification_report(y_test, y_pred, target_names=data_info.target_names))
# Feature Visualization
st.subheader("📈 Visualizing with 2 Features")
feature_x = st.selectbox("X-axis Feature", df.columns[:-1], index=0)
feature_y = st.selectbox("Y-axis Feature", df.columns[:-1], index=1)
X_vis = df[[feature_x, feature_y]]
X_vis_scaled = scaler.fit_transform(X_vis)
X_train_vis, X_test_vis, y_train_vis, y_test_vis = train_test_split(X_vis_scaled, y, test_size=0.2, random_state=42)
model_vis = SVC(kernel=kernel, C=C)
model_vis.fit(X_train_vis, y_train_vis)
# Decision Boundary
h = 0.02
x_min, x_max = X_vis_scaled[:, 0].min() - 1, X_vis_scaled[:, 0].max() + 1
y_min, y_max = X_vis_scaled[:, 1].min() - 1, X_vis_scaled[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = model_vis.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
fig, ax = plt.subplots(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3, cmap="coolwarm")
sns.scatterplot(x=X_vis_scaled[:, 0], y=X_vis_scaled[:, 1], hue=df["target"], palette="coolwarm", ax=ax)
plt.xlabel(feature_x)
plt.ylabel(feature_y)
plt.title("SVM Decision Boundary")
st.pyplot(fig)
# Summary
st.markdown("""
---
## 💡 Summary
- SVM creates a hyperplane that separates classes.
- Works well for small and high-dimensional datasets.
- The `C` parameter controls the trade-off between margin and misclassification.
### Tips:
- Use **RBF kernel** for non-linear data.
- Try adjusting C to see how the margin changes.
""")
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